1. Field of the Invention
The present invention relates to a method for determining treatment duration, and in particular to determining optimal treatment duration, such as for determining an optimal chemotherapeutic dose schedule [CDS], based on a time of maximum treatment effectiveness deduced from measurements.
2. Description of the Related Art
Some treatments include toxic side effects that cause harm to a patient. Effective use of such treatments requires the careful balancing of toxicity and benefit from the treatment. Many parameters influence this balance. In past approaches, effective treatment is determined by trial and error. Parameters of import are identified, such as the treatment administered (such as drug or radiation), dose of the treatment, duration of a cycle during which the treatment is administered, number of cycles, interval between the start of successive cycles (including the duration of any treatment hiatus), remedial treatment between cycles, and series of different treatments. Clinical trials are performed with a subset of all possible combinations of these factors. Clinical trials that produce the most favorable result of sufficient benefit then become an accepted protocol. However, there is rarely a determination made that any parameter of the treatment is optimal.
For example, the treatment of some cancers, including breast cancer, include the administration of one or more chemotherapeutic agents on a chemotherapeutic dose schedule (CDS) that specifies the agent, the dose of the agent, the duration of an administration cycle and an interval between the starts of successive cycles, and sometimes a number of cycles. CDS is usually determined by time-consuming, costly laboratory and clinical experiments. Optimizing CDS more efficiently than trial and error could maximize the benefit/toxicity ratio of any chemotherapeutic agent or combination of agents.
Computational models of cancer chemotherapy have the potential to streamline clinical trial design, contribute to the design of rational, tailored treatments, and facilitate our understanding of experimental results.
In one approach by L. Norton, the Gompertzian model of population growth is shown to apply to cancer cells. As a result, the Norton-Simon hypothesis was formulated which predicts that a relative advantage can be achieved if tumors are treated when they are smaller rather than larger. This leads to the conclusion that it is better to reduce the time between treatments, and thus limit the time that tumors have to increase their size. The more frequent application of chemotherapy is called a dose dense regimen for the treatment of tumors. In this regimen it is preferable to apply an effective dose of a chemotherapy agent for a shorter time so that the patient can recover more quickly and thus receive the next cycle more quickly.
Dose dense regimens have been used before, for example, in chemotherapeutic treatment of breast cancer. U.S. Patent Application 20040229826, by L. Norton, the entire contents of each of which are hereby incorporated by reference as if fully set forth herein. Norton showed that when one is administering to the patient one or more types of chemotherapeutic agent in a plurality of treatments, the results are improved by making use of the optimal amount of each type of chemotherapeutic agent and giving the treatments in a dose-dense protocol, preferably at the shortest tolerated intervals. It was shown that disease-free survival (DFS) was significantly prolonged for the dose-dense regimens compared with typical regimens that start a cycle every three weeks (“three-week regimens”). Further, overall survival (OS) was significantly prolonged in the dose-dense regimens, even after adjusting for the standard clinical pretreatment variables mentioned previously. The DFS and OS advantages of dose density were not accompanied by an increase in toxicity. Thus sequential chemotherapy that maintains dose density preserved efficacy while reducing toxicity. Sequential dose-dense single agent therapy could permit the rapid integration of new drugs into therapeutic regimens, including biological agents.
The selection of a value for duration of administration of treatment on each cycle is an important step in the application of the dose dense treatment. Trial and error methods, such as were used in past studies, to select the cycle duration are themselves time consuming and expensive. Computational models have the potential to speed the determination of the duration for a treatment cycle.
Based on the preceding discussion, there is a clear need for a method to determine a more optimal duration of treatment administration for each cycle of treatment, without a large number of clinical trials. In particular there is a need for application of a computational model to interpret measurements to determine the duration of a treatment cycle.